The aim of this paper is to develop a simulation framework to minimise the cost associated with commercial greenhouse yields (production cost). Greenhouse energy demand and yield production models are coupled with a cost model to investigate the interaction between energy consumption costs and yield production benefits. The coupled model is defined as an optimisation problem to determine the optimal values of decision variables (e.g. cover material, heating and cooling technology, inside temperature and relative humidity set points, plant density, etc.), which result in minimum production cost as the objective function. The results show that the integrated energy demand-crop yield production model outperforms the “classical approach” of a separate energy consumption model where yield is not explicitly modelled but only considered by constraints warranting good growing conditions. In the case of a tomato greenhouse in Iran, the financial cost and environmental impact of producing a 1 kg yield based on integrated energy demand-yield production model were 49% and 29% lower than the energy-consumption model alone. Moreover, the impact of fuel prices on the optimisation results is investigated by use of the proposed framework. Considering real fuel prices instead of current subsidised fuel prices in the Iranian case study, 70% of energy consumption and 19% of climate change impacts could be eliminated while increasing the production cost by 17%. Selling the saved fuel at real prices to other customers would provide the government with more than enough money to compensate the farmers for the increased yield cost.

中文翻译：

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基于综合能源需求-产出生产模型的节能温室优化

本文的目的是开发一个模拟框架，以最大限度地减少与商业温室产量相关的成本（生产成本）。温室能源需求和产量生产模型与成本模型相结合，以研究能源消耗成本与产量生产效益之间的相互作用。耦合模型被定义为一个优化问题，以确定决策变量（例如覆盖材料、加热和冷却技术、内部温度和相对湿度设定点、植物密度等）的最佳值，从而使生产成本最低目标函数。结果表明，综合能源需求 - 作物产量生产模型优于单独能源消耗模型的“经典方法”，其中产量没有明确建模，但仅考虑保证良好生长条件的约束条件。以伊朗的番茄温室为例，基于综合能源需求-收益生产模型生产 1 公斤产量的财务成本和环境影响比单独的能源消耗模型低 49% 和 29%。此外，使用所提出的框架研究了燃料价格对优化结果的影响。考虑到实际燃料价格而不是伊朗案例研究中当前的补贴燃料价格，可以消除 70% 的能源消耗和 19% 的气候变化影响，同时将生产成本增加 17%。